
import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation




def 					__magic_name__					(  __lowerCAmelCase      :		List[str]				)     ->  Optional[Any]:
				__lowerCamelCase          =      384
				__lowerCamelCase          =      7
				if "tiny" in model_name:
								__lowerCamelCase          =      96
								__lowerCamelCase          =      (2, 2, 6, 2)
								__lowerCamelCase          =      (3, 6, 12, 24)
				elif "small" in model_name:
								__lowerCamelCase          =      96
								__lowerCamelCase          =      (2, 2, 18, 2)
								__lowerCamelCase          =      (3, 6, 12, 24)
				elif "base" in model_name:
								__lowerCamelCase          =      128
								__lowerCamelCase          =      (2, 2, 18, 2)
								__lowerCamelCase          =      (4, 8, 16, 32)
								__lowerCamelCase          =      12
								__lowerCamelCase          =      512
				elif "large" in model_name:
								__lowerCamelCase          =      192
								__lowerCamelCase          =      (2, 2, 18, 2)
								__lowerCamelCase          =      (6, 12, 24, 48)
								__lowerCamelCase          =      12
								__lowerCamelCase          =      768

				# set label information
				__lowerCamelCase          =      150
				__lowerCamelCase          =      '''huggingface/label-files'''
				__lowerCamelCase          =      '''ade20k-id2label.json'''
				__lowerCamelCase          =      json.load(open(hf_hub_download(_lowerCamelCase						,    _lowerCamelCase						,    repo_type='''dataset'''				)						,    '''r'''				)				)
				__lowerCamelCase          =      {int(_lowerCamelCase				): v for k, v in idalabel.items()}
				__lowerCamelCase          =      {v: k for k, v in idalabel.items()}

				__lowerCamelCase          =      SwinConfig(
				    embed_dim=_lowerCamelCase						,    depths=_lowerCamelCase						,    num_heads=_lowerCamelCase						,    window_size=_lowerCamelCase						,    out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']						,    )
				__lowerCamelCase          =      UperNetConfig(
				    backbone_config=_lowerCamelCase						,    auxiliary_in_channels=_lowerCamelCase						,    num_labels=_lowerCamelCase						,    idalabel=_lowerCamelCase						,    labelaid=_lowerCamelCase						,    )

				return config




def 					__magic_name__					(  __lowerCAmelCase      :		List[str]				)     ->  List[str]:
				__lowerCamelCase          =      []

				# fmt: off
				# stem
				rename_keys.append(('''backbone.patch_embed.projection.weight''', '''backbone.embeddings.patch_embeddings.projection.weight''')				)
				rename_keys.append(('''backbone.patch_embed.projection.bias''', '''backbone.embeddings.patch_embeddings.projection.bias''')				)
				rename_keys.append(('''backbone.patch_embed.norm.weight''', '''backbone.embeddings.norm.weight''')				)
				rename_keys.append(('''backbone.patch_embed.norm.bias''', '''backbone.embeddings.norm.bias''')				)
				# stages
				for i in range(len(config.backbone_config.depths				)				):
								for j in range(config.backbone_config.depths[i]				):
												rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.norm1.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')				)
												rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.norm1.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')				)
												rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')				)
												rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')				)
												rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')				)
												rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')				)
												rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.norm2.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')				)
												rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.norm2.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')				)
												rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')				)
												rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')				)
												rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight''')				)
												rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias''')				)

								if i < 3:
												rename_keys.append((f'''backbone.stages.{i}.downsample.reduction.weight''', f'''backbone.encoder.layers.{i}.downsample.reduction.weight''')				)
												rename_keys.append((f'''backbone.stages.{i}.downsample.norm.weight''', f'''backbone.encoder.layers.{i}.downsample.norm.weight''')				)
												rename_keys.append((f'''backbone.stages.{i}.downsample.norm.bias''', f'''backbone.encoder.layers.{i}.downsample.norm.bias''')				)
								rename_keys.append((f'''backbone.norm{i}.weight''', f'''backbone.hidden_states_norms.stage{i+1}.weight''')				)
								rename_keys.append((f'''backbone.norm{i}.bias''', f'''backbone.hidden_states_norms.stage{i+1}.bias''')				)

				# decode head
				rename_keys.extend(
				    [
				        ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
				        ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
				        ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
				        ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
				    ]				)
				# fmt: on

				return rename_keys




def 					__magic_name__					(  __lowerCAmelCase      :		List[Any]						,    __lowerCAmelCase      :		Tuple						,    __lowerCAmelCase      :		List[Any]				)     ->  str:
				__lowerCamelCase          =      dct.pop(_lowerCamelCase				)
				__lowerCamelCase          =      val




def 					__magic_name__					(  __lowerCAmelCase      :		Union[str, Any]						,    __lowerCAmelCase      :		Optional[int]				)     ->  str:
				__lowerCamelCase          =      [int(backbone_config.embed_dim * 2**i				) for i in range(len(backbone_config.depths				)				)]
				for i in range(len(backbone_config.depths				)				):
								__lowerCamelCase          =      num_features[i]
								for j in range(backbone_config.depths[i]				):
												# fmt: off
												# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
												__lowerCamelCase          =      state_dict.pop(f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight'''				)
												__lowerCamelCase          =      state_dict.pop(f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias'''				)
												# next, add query, keys and values (in that order) to the state dict
												__lowerCamelCase          =      in_proj_weight[:dim, :]
												__lowerCamelCase          =      in_proj_bias[: dim]
												__lowerCamelCase          =      in_proj_weight[
												    dim : dim * 2, :
												]
												__lowerCamelCase          =      in_proj_bias[
												    dim : dim * 2
												]
												__lowerCamelCase          =      in_proj_weight[
												    -dim :, :
												]
												__lowerCamelCase          =      in_proj_bias[-dim :]
												# fmt: on




def 					__magic_name__					(  __lowerCAmelCase      :		List[str]				)     ->  int:
				__lowerCamelCase   ,       __lowerCamelCase          =      x.shape
				__lowerCamelCase          =      x.reshape(_lowerCamelCase						,    4						,    in_channel // 4				)
				__lowerCamelCase          =      x[:, [0, 2, 1, 3], :].transpose(1						,    2				).reshape(_lowerCamelCase						,    _lowerCamelCase				)
				return x




def 					__magic_name__					(  __lowerCAmelCase      :		List[Any]				)     ->  Any:
				__lowerCamelCase   ,       __lowerCamelCase          =      x.shape
				__lowerCamelCase          =      x.reshape(_lowerCamelCase						,    in_channel // 4						,    4				)
				__lowerCamelCase          =      x[:, :, [0, 2, 1, 3]].transpose(1						,    2				).reshape(_lowerCamelCase						,    _lowerCamelCase				)

				return x




def 					__magic_name__					(  __lowerCAmelCase      :		Dict				)     ->  List[str]:
				__lowerCamelCase          =      x.shape[0]
				__lowerCamelCase          =      x.reshape(4						,    in_channel // 4				)
				__lowerCamelCase          =      x[[0, 2, 1, 3], :].transpose(0						,    1				).reshape(_lowerCamelCase				)
				return x




def 					__magic_name__					(  __lowerCAmelCase      :		Union[str, Any]				)     ->  int:
				__lowerCamelCase          =      x.shape[0]
				__lowerCamelCase          =      x.reshape(in_channel // 4						,    4				)
				__lowerCamelCase          =      x[:, [0, 2, 1, 3]].transpose(0						,    1				).reshape(_lowerCamelCase				)
				return x




def 					__magic_name__					(  __lowerCAmelCase      :		Optional[Any]						,    __lowerCAmelCase      :		List[Any]						,    __lowerCAmelCase      :		str				)     ->  str:
				__lowerCamelCase          =      {
				    '''upernet-swin-tiny''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth''',
				    '''upernet-swin-small''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth''',
				    '''upernet-swin-base''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth''',
				    '''upernet-swin-large''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth''',
				}
				__lowerCamelCase          =      model_name_to_url[model_name]
				__lowerCamelCase          =      torch.hub.load_state_dict_from_url(_lowerCamelCase						,    map_location='''cpu'''						,    file_name=_lowerCamelCase				)[
				    '''state_dict'''
				]

				for name, param in state_dict.items():
								print(_lowerCamelCase						,    param.shape				)

				__lowerCamelCase          =      get_upernet_config(_lowerCamelCase				)
				__lowerCamelCase          =      UperNetForSemanticSegmentation(_lowerCamelCase				)
				model.eval()

				# replace "bn" => "batch_norm"
				for key in state_dict.copy().keys():
								__lowerCamelCase          =      state_dict.pop(_lowerCamelCase				)
								if "bn" in key:
												__lowerCamelCase          =      key.replace('''bn'''						,    '''batch_norm'''				)
								__lowerCamelCase          =      val

				# rename keys
				__lowerCamelCase          =      create_rename_keys(_lowerCamelCase				)
				for src, dest in rename_keys:
								rename_key(_lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase				)
				read_in_q_k_v(_lowerCamelCase						,    config.backbone_config				)

				# fix downsample parameters
				for key, value in state_dict.items():
								if "downsample" in key:
												if "reduction" in key:
																__lowerCamelCase          =      reverse_correct_unfold_reduction_order(_lowerCamelCase				)
												if "norm" in key:
																__lowerCamelCase          =      reverse_correct_unfold_norm_order(_lowerCamelCase				)

				model.load_state_dict(_lowerCamelCase				)

				# verify on image
				__lowerCamelCase          =      '''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
				__lowerCamelCase          =      Image.open(requests.get(_lowerCamelCase						,    stream=_lowerCamelCase				).raw				).convert('''RGB'''				)

				__lowerCamelCase          =      SegformerImageProcessor()
				__lowerCamelCase          =      processor(_lowerCamelCase						,    return_tensors='''pt'''				).pixel_values

				with torch.no_grad():
								__lowerCamelCase          =      model(_lowerCamelCase				)
								__lowerCamelCase          =      outputs.logits

				print(logits.shape				)
				print('''First values of logits:'''						,    logits[0, 0, :3, :3]				)
				# assert values
				if model_name == "upernet-swin-tiny":
								__lowerCamelCase          =      torch.tensor(
								    [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]				)
				elif model_name == "upernet-swin-small":
								__lowerCamelCase          =      torch.tensor(
								    [[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]]				)
				elif model_name == "upernet-swin-base":
								__lowerCamelCase          =      torch.tensor(
								    [[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]]				)
				elif model_name == "upernet-swin-large":
								__lowerCamelCase          =      torch.tensor(
								    [[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]]				)
				print('''Logits:'''						,    outputs.logits[0, 0, :3, :3]				)
				assert torch.allclose(outputs.logits[0, 0, :3, :3]						,    _lowerCamelCase						,    atol=1E-4				)
				print('''Looks ok!'''				)

				if pytorch_dump_folder_path is not None:
								print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''				)
								model.save_pretrained(_lowerCamelCase				)
								print(f'''Saving processor to {pytorch_dump_folder_path}'''				)
								processor.save_pretrained(_lowerCamelCase				)

				if push_to_hub:
								print(f'''Pushing model and processor for {model_name} to hub'''				)
								model.push_to_hub(f'''openmmlab/{model_name}'''				)
								processor.push_to_hub(f'''openmmlab/{model_name}'''				)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__  :     Dict   				=       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--model_name",
					    default="upernet-swin-tiny",
					    type=str,
					    choices=[F'upernet-swin-{size}' for size in ["tiny", "small", "base", "large"]],
					    help="Name of the Swin + UperNet model you'd like to convert.",
					)
					parser.add_argument(
					    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
					)
					parser.add_argument(
					    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
					)

					SCREAMING_SNAKE_CASE__  :     List[str]   				=       parser.parse_args()
					convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase        =				{
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """RobertaPreLayerNormForCausalLM""",
					    """RobertaPreLayerNormForMaskedLM""",
					    """RobertaPreLayerNormForMultipleChoice""",
					    """RobertaPreLayerNormForQuestionAnswering""",
					    """RobertaPreLayerNormForSequenceClassification""",
					    """RobertaPreLayerNormForTokenClassification""",
					    """RobertaPreLayerNormModel""",
					    """RobertaPreLayerNormPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFRobertaPreLayerNormForCausalLM""",
					    """TFRobertaPreLayerNormForMaskedLM""",
					    """TFRobertaPreLayerNormForMultipleChoice""",
					    """TFRobertaPreLayerNormForQuestionAnswering""",
					    """TFRobertaPreLayerNormForSequenceClassification""",
					    """TFRobertaPreLayerNormForTokenClassification""",
					    """TFRobertaPreLayerNormMainLayer""",
					    """TFRobertaPreLayerNormModel""",
					    """TFRobertaPreLayerNormPreTrainedModel""",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """FlaxRobertaPreLayerNormForCausalLM""",
					    """FlaxRobertaPreLayerNormForMaskedLM""",
					    """FlaxRobertaPreLayerNormForMultipleChoice""",
					    """FlaxRobertaPreLayerNormForQuestionAnswering""",
					    """FlaxRobertaPreLayerNormForSequenceClassification""",
					    """FlaxRobertaPreLayerNormForTokenClassification""",
					    """FlaxRobertaPreLayerNormModel""",
					    """FlaxRobertaPreLayerNormPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_roberta_prelayernorm import (
					    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    RobertaPreLayerNormConfig,
					    RobertaPreLayerNormOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_roberta_prelayernorm import (
										    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RobertaPreLayerNormForCausalLM,
										    RobertaPreLayerNormForMaskedLM,
										    RobertaPreLayerNormForMultipleChoice,
										    RobertaPreLayerNormForQuestionAnswering,
										    RobertaPreLayerNormForSequenceClassification,
										    RobertaPreLayerNormForTokenClassification,
										    RobertaPreLayerNormModel,
										    RobertaPreLayerNormPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_roberta_prelayernorm import (
										    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRobertaPreLayerNormForCausalLM,
										    TFRobertaPreLayerNormForMaskedLM,
										    TFRobertaPreLayerNormForMultipleChoice,
										    TFRobertaPreLayerNormForQuestionAnswering,
										    TFRobertaPreLayerNormForSequenceClassification,
										    TFRobertaPreLayerNormForTokenClassification,
										    TFRobertaPreLayerNormMainLayer,
										    TFRobertaPreLayerNormModel,
										    TFRobertaPreLayerNormPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_roberta_prelayernorm import (
										    FlaxRobertaPreLayerNormForCausalLM,
										    FlaxRobertaPreLayerNormForMaskedLM,
										    FlaxRobertaPreLayerNormForMultipleChoice,
										    FlaxRobertaPreLayerNormForQuestionAnswering,
										    FlaxRobertaPreLayerNormForSequenceClassification,
										    FlaxRobertaPreLayerNormForTokenClassification,
										    FlaxRobertaPreLayerNormModel,
										    FlaxRobertaPreLayerNormPreTrainedModel,
										)

else:
					import sys

					UpperCAmelCase        =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


